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Lecture
Linear Systems: Iterative Methods
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Related lectures (29)
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Conjugate Gradient Method: Iterative Optimization
Covers the conjugate gradient method, stopping criteria, and convergence properties in iterative optimization.
Iterative Methods for Linear Equations
Introduces iterative methods for solving linear equations and discusses the gradient method for minimizing errors.
Iterative Methods for Linear Equations
Covers iterative methods for solving linear equations and analyzing convergence, including error control and positive definite matrices.
Regression & Systemed Lineaires
Covers the principles of regression and linear systems, focusing on iterative methods.
Linear Systems: Convergence and Methods
Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.
Iterative Methods: Linear Systems
Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.
Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Conjugate Gradient Method
Covers the Conjugate Gradient method for solving linear systems efficiently.
Linear Systems: Iterative Methods
Covers iterative methods for solving linear systems, including Jacobi and Gauss-Seidel methods.
Vectorization in Python: Efficient Computation with Numpy
Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.
